Key Predictive Features in the Emergency Department for Healthcare-Associated Infections

Key Predictive Features in the Emergency Department for Healthcare-Associated Infections | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Key Predictive Features in the Emergency Department for Healthcare-Associated Infections Andrea Fabbri, Ayca Begum Tascioglu, Flavio Bertini, Barbara Benazzi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7536446/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Overcrowding, prolonged stays and invasive interventions could increase the risk of healthcare-associated infections (HAIs) in Emergency Departments (ED). Aim of study was to investigate whether the risk of developing a HAI can be estimated in patients at entry on the basis of ED visit data, and whether they are associated with poorer outcome. Methods This retrospective single centre study included subjects who required urgent hospitalisation following ED visit between 2017 and 2022. Main outcome measures considered were the occurrence of late HAIs and the cumulative survival rate at 30 days. The key predictive features tested in a logistic model were age, sex, vital parameters as measured by the National Early Warning Score (NEWS), priority levels upon entry, comorbidities by the Charlson Comorbidity Index (CCI), trauma related diseases, main diagnosis and ED length of stay. Results In 2,556 (8,9%) out of 28,803 hospitalised patients aged 73 [ 17 ] years (mean [SD]) a diagnosis of HAI was recorded during hospital stays. In order of frequency, HAIs occurred in bloodstream (4.7%), in urinary (3.4%), respiratory (2.9%), gastrointestinal (0.4%) tract, or in surgical (0.3%) and skin and soft tissue (0.05%) sites. Main features selected by the logistic model in the prediction of HAI were infectious and parasitic diseases, CCI > 4, genitourinary system diseases, CCI 3 to 4, COVID period, priority level at arrival in ED. In-hospital cumulative survival rate in HAI group was reduced, namely for subjects with pneumonia and bloodstream infections. Conclusions A group of key characteristics in subjects visiting the ED can predict the onset of nosocomial infections that negatively affect survival, particularly for patients who develop pneumonia or bloodstream infections. Older Age Predictors Comorbidity Survival Rate Nosocomial Infections Figures Figure 1 Figure 2 Figure 3 Background Healthcare-associated infections (HAIs), usually acquired in hospitals, are becoming a serious threat to healthcare systems [ 1 ]. Subjects with HAIs often have a longer hospital stay and experience adverse events during treatment, which can be caused by antibiotic resistance [ 2 ]. This can lead to higher mortality rates and put a huge strain on healthcare systems [ 3 , 4 ]. The European Centre for Disease Prevention and Control (ECDC) annual surveillance reports [ 1 ] indicate that HAIs are most commonly pneumonia (PN), followed by urinary tract infections (UT), surgical site (SS) infections, bloodstream infections (BS), gastrointestinal infections (GI) and skin and soft tissue (SST) infections. Risk factors include advanced age (> 50 years), invasive procedures, a high number of comorbidities, prolonged hospital stays, immunosuppression, non-appropriate use of antibiotics and multiple hospital transfers [ 5 ]. Poor hand hygiene, inadequate aseptic techniques, and suboptimal environmental cleaning may further increase the risk [ 6 ]. A crowded environment, such as an ED, in which many patients with different complaints and undiagnosed diseases coexist for long periods in the same environment, can certainly increase the risk [ 7 ][ 8 ]. Identification of subjects at risk could allow for early diagnosis and treatment, thus reducing the impact of later complications and unfavorable outcome [ 9 ]. This study aims to identify key clinical features in subjects visited in ED that predict the onset of nosocomial infections, based on the assumption that these subjects would have a worse outcome during their hospital stay. Methods Study Design and Setting During the study period (1 January 2017–31 December 2022), 283,385 electronic medical records from the ED of Morgagni-Pierantoni Hospital (a 3rd-level hospital 460 beds and serving a population of 200,000) in Forlì, Italy, were evaluated. The analysis was performed on all hospitalised subjects after arrival in the ED, using a link between ED and inpatient records. 232,194 were excluded (Fig. 1 ): 4,115 discharged homes directly from the ED; 4,064 died in the ED; 3,646 < 18 years; 2,103 insufficient data. A further 7,366 were excluded due to a hospital stay < 3 days; 1,094 due to missing follow-up data. The final analysis was performed on a sample of 28,803 for whom all information from the initial ED visit to complete hospital records were available. Data Collection Most baseline patient characteristics, including age (for decades) and sex, were collected at the time of ED registration. Vital signs at arrival in ED, specifically systolic blood pressure (SBP), heart rate (HR), respiratory rate (RR), and temperature, were recorded upon arrival to calculate the National Early Warning Score (NEWS) [ 10 ]. NEWS was computed and considered as a categorical variable (0–4 low risk, 5–6 medium risk, > 6 high risk). The Charlson Comorbidity Index (CCI) [ 11 ] was calculated using free-text patient reports extracted during the ED visit. The CCI was categorised as follows (mild, 1–2; moderate, 3–4; severe, > 4), using selection criteria and disease categories recently validated [ 12 ]. Additionally, age adjustments were applied, with each decade over 40 years adding 1 point (e.g., 50–59 years, + 1; 60–69 years, + 2; 70–79 years, + 3, etc.), with these "age points" added to the total CCI score [ 13 ]. Outcome Measures HAIs during hospital stay were considered as main outcome measure, according to the ECDC disease categories: pneumonia (PN), bloodstream (BS) infections, urinary tract (UT) infections, gastrointestinal (GI) infections, surgical site (SS) infections and skin and soft tissue (SST) infections Table 3 [ 1 , 8 ]. To reduce false positives, excluded were patients staying less than 3 days [ 1 ]. As HAI diagnoses were not associated with a definite date of identification, we estimated them by information derived from clinical, laboratory, microbiological and radiological reports in the electronic medical record. If a patient had more than one HAI, it was counted only once. Table 3 Healthcare associated infections (HAI) by category in subjects visited in ED. Data are reported in order of frequency as number of cases and percent (%). Patients with multiple HAI categories are counted only once in the analysis. HAI categories No. = 2,256 % Blood Stream (BS) 995.91, 995.92 Sepsis 592 26.2 038, 038.4 Septicemia (including specific organisms) 504 22.3 038.8, 038.9, 790.7 Septicemia unspecified, bacteriemia 139 6.2 038.1 Septicemia due to Streptococcus 97 4.3 038.0 Septicemia due to Staphylococcus Aureus 16 0.7 038.2 Septicemia due to Gram neg. bacteria 2 0.1 Total 1,350 59.8 Urinary tract (UT) 599.0 Urinary infection (unspecified site) 931 41.3 590.1 Acute pyelonephritis 30 1.3 996.64 Infection due to urinary catheter 18 0.8 Total 979 43.4 Pneumonia (PN) 485 Bronchopneumonia, organism unspecified 376 16.7 460–466 Acute & lower respiratory tract infections 151 6.7 486 Pneumonia, organism unspecified 110 4.9 480 Viral pneumonia 101 4.5 482, 483, 484.7, 484.8 Pneumonia due to other organism, diseases 89 3.9 481 Pneumococcal pneumonia 21 0.9 Total 848 37.6 Gastrointestinal (GI) 008.45 Intestinal infection (Clostridium difficile) 88 3.9 009.0, 009.1 Infectious enteritis, unspecified 39 1.7 008.5x, 009.2, 099.3 Other intestinal infections due to bacteria 3 0.1 Total 130 5.8 Surgical Site (SS) 996.6x Infection due to internal prosthetic device 55 2.4 998.59 Other post-operative infection 25 1.1 682.xx Cellulitis and abscess (local SSI spread) 14 0.6 998.50-4, 998.51 Post operative wound infection, 0 0.0 Total 94 4.2 Skin & Soft Tissue (SST) 680–686 Other cellulitis and abscess 14 0.6 Data analysis Data analysis was conducted on a cohort of 28,803 adult patients (> 18 years). Data were summarised as counts and percentages. Continuous variables were reported as either the mean (standard deviation, SD) or median [interquartile range, IQR]. Differences in patient characteristics, along with the corresponding 95% confidence intervals (CIs), were calculated using the Agresti-Caffo method [ 14 ]. For all statistical analyses, a p-value of less than 0.001 was considered significant. The primary outcome measure was any HAI. Demographic characteristics considered were: age, sex and comorbidities. Comorbidities were considered both as individual variables and as a categorised value of CCI > 4. Additional variables tested were NEWS, ICD9-CM diagnosis codes, trauma and non-trauma related visits, ED length of stay (EDLoS) calculated as the difference between entry and exit times, as categorical values ( 24 hours), and different periods, pre- (2017–2019) and COVID restriction (2020–2022) periods. A generalised linear mixed regression model was developed, adjusting for main characteristics such as age, sex, high CCI > 4, NEWS > 6 and trauma-related diseases. In a second step, the model was expanded to include the EDLoS, COVID period and main ICD9-CM diagnosis codes. The odds ratio (OR) and 95% confidence intervals (95% CI) were calculated. A score for mortality risk was calculated for each patient based on the coefficients computed by the logistic regression derived from variables entering the stepwise procedure. The accuracy of such a risk score was then evaluated by the area under the receiver operating characteristic (ROC) curves. The optimal cutoff point, i.e., the value that simultaneously maximises the sensitivity and the specificity, was calculated by the Youden index [ 15 ]. The Kaplan-Meier curve was reported to estimate the probability of survival at 30 days. The curve is plotted with time on the x-axis and the estimated probability of survival on the y-axis [ 16 ]. The log-rank test (Mantel–Cox test) was used to compare the survival distributions between different categories and the overall survival rate of the HAI group. To avoid ambiguity, no synthetic data were generated to address missing data; hence, the analysis utilised only complete cases. The statistical model was ultimately validated using event-number balancing techniques [ 17 ]. For feature selection, stepwise feature selection is used. Using a two-tailed binomial test, we estimated that, for a study with adequate statistical power and an acceptable margin of error of ± 1%, assuming a significance level (α) of 5%, a power of 80%, and an expected prevalence of HAI cases of 7.7%, as reported in the ECDC last publication [ 1 ], the required initial sample size would be at least 2,731 patients, a lower number of cases than what was considered. The logistic model used a rule of thumb of at least 10 events per variable. A model with 10 to 20 predictors would require at least 200 cases of HAI. Assuming an incidence rate of 7.7%, this would be about 2,600 patients, in line with the previous estimate. Statistical analyses were conducted in Python 3.10.12 using the following libraries: pandas (2.2.3), numpy (1.24.1), scipy (1.10.0), scikit-learn (1.6.0), seaborn (0.12.2), matplotlib (3.6.3), statsmodels (0.13.5), and lifelines (0.30.0). Results Study Subjects The analysis was conducted on 28,803 patients (mean age, 73 years; SD, 17). Table 1 shows patients’ characteristics comparing HAI vs. non-HAI groups. A total of 2,556 cases (8.9%) belonged to the HAI group. There were no significant differences between men and women or between age groups, except that subjects over 80 were more represented in the HAI group. Patients in the HAI group were characterized by a higher NEWS (Table 1 ), as well as a higher comorbidity index (CCI), particularly when the CCI was greater than 4 (Table 1 ). The list of comorbidities is shown in Table A1. The most common comorbidities were diabetes mellitus (DM), heart failure (HF), dementia (D), chronic obstructive pulmonary disease (COPD), coronary artery disease (CAD) and chronic kidney failure (CKD); all of them were more prevalent in the HAI group ( Table A1 ). Length of stay in ED was less than 6 hours in most cases (99.5%), with no difference between groups. Table 1 Baseline characteristics at entry in ED in relation to the clinical profile of subjects with/without hospital acquired infections (HAIs). Total, No. HAI group, No. (%) Non-HAI group, No. (%) Odds Ratio (95% CI) P-value Patients 28,803 2,556 (8.9) 26,247 (91.1) -- -- Sex (males) 14,365 (49.9) 1,281 (50.1) 13,084 (49.8) 1.01 (0.93–1.10) 0.796 Age (years) 18–30 853 (3.0) 25 (1.0) 828 (3.1) 0.30 (0.20–0.45) < 0.001 31–40 896 (3.1) 28 (1.1) 868 (3.3) 0.32 (0.22–0.47) < 0.001 41–50 1,659 (5.8) 82 (3.2) 1,577 (6.0) 0.52 (0.41–0.65) < 0.001 51–60 2,558 (8.9) 160 (6.3) 2,398 (9.1) 0.66 (0.56–0.78) 80 12,019 (41.7) 1,337 (52.3) 10,682 (40.7) 1.60 (1.47–1.73) 6 2,997 (10.4) 294 (11.5) 2,703 (10.3) 1.13 (1.00–1.29) 0.057 CCI 1–2 6,563 (22.8) 311 (12.1) 6,252 (23.8) 0.44 (0.39–0.50) 4 13,189 (45.8) 1,501 (58.7) 11,688 (44.5) 1.77 (1.63–1.92) < 0.001 Data are reported as N. of cases (%), difference as Odds Ratio (OR) with 95% Confidence Intervals (95% CI). NEWS: New Early Warning Score, CCI: Charlson Comorbidity Index. Percent of subjects referred to the total number of cases. P value < 0.001 for significance. Main diagnoses in ED, divided by ICD-9 CM category, are reported in Table A2 . They were in order of frequency, circulatory system diseases (22.1%), respiratory system diseases (19.2%), digestive system diseases (14.5%), and injuries and poisonings (12.5%). The prevalence of infectious and parasitic diseases and genitourinary diseases was higher in the HAI group, whereas respiratory diseases, injuries, poisonings and digestive diseases were more prevalent in the non-HAI group (Table 2 ). Table 2 List of selected variables entered the logistic model for hospital acquired infections (HAI) reported in order of relevance. Charlson Comorbidity Index (CCI). Variables were considered as categorical and data are reported as Odds Ratio (OR) with 95% Confidence Intervals (95%CI); P value for significance < 0.05. Independent variables OR (95% CI) p-value Infectious & Parasitic Diseases (001-139) 3.94 (3.09 to 5.04) 4 (severe) 2.13 (1.75 to 2.58) < 0.001 Diseases of the Genitourinary System (580–629) 1.98 (1.55 to 2.53) < 0.001 CCI 3–4 (moderate) 1.66 (1.35 to 2.05) < 0.001 COVID-19 Period 1.37 (1.19 to 1.57) < 0.001 Priority Level 1.27 (1.10 to 1.45) < 0.001 Not selected variables: length of stay in ED, NEWS, trauma, ICD9-CM diagnosis codes of neoplasm (140–239), endocrine, nutritional, and metabolic diseases (240–279), diseases of the blood (280–289), mental disorders (290–319), diseases of the nervous system (320–389), diseases of the circulatory system (390–459), diseases of respiratory system (460–519), diagnosis codes of digestive system (520–579), complications of pregnancy (630–679), diseases of the skin (680–709), diseases of the musculoskeletal system (710–739), congenital malformations (740–759), symptoms, signs, and laboratory findings (780–799), injury, poisoning (800–999), external causes (E, V code). Main Results Table 2 and Fig. 2 represent the list of key predictive features selected by the stepwise procedure of the logistic model to predict nosocomial infections. In order of importance, they were: CCI score > 4, infectious and parasitic diseases, the CCI score 2–4, diseases of the genito-urinary system, the period of COVID pandemic, and priority level at entry. Figure A1 shows the ROC curve for the risk score of HAI occurrence. The accuracy of predicting HAI was 0.697 ± 0.011, with a sensitivity of 0.63 and a specificity of 0.67 at an optimal cut-off point (the best sensitivity at the best specificity) of 0.517. Figure 3 shows the estimated 30-day Kaplan–Meier cumulative survival rate. The cumulative survival rate was lower in the HAI group than in the non-HAI group (log-rank test P-value < 0.001) ( A area ), and cases in the bloodstream infection and pneumonia categories had a significantly lower survival rate compared to the survival of the overall HAI group ( B area ). DISCUSSION This is the largest study to date on predicting HAI in ED patients. The risk is associated with specific conditions, e.g., infectious/parasitic diseases, high comorbidity index, genito-urinary system diseases, the pandemic period, and high priority at presentation to the ED. Survival rates for those with HAI, particularly bloodstream and pneumonia, were lower. These findings emphasise the importance of identifying risk factors for nosocomial infections to improve patient outcomes. Older age, higher comorbidity burden and higher illness severity were associated with diagnosis of HAI [ 17 ].The results of this study demonstrate that this pattern of key features is valid for predicting the risk of HAI, independently of the reason(s) for attending ED for different complaints. The study protocol excluded 7,885 cases (16.7%) from the analysis cases as the hospital stay was < 3 days (Fig. 1 ). This decision was made both to reduce the confounding effect of cases with infection already present at the time of admission [ 18 ] The percentage of cases with a diagnosis of HAI at admission is probably not negligible: the ECDC in the last report estimates this percentage ranges from 15.6% (Czech Republic) to 41.2% (Sweden) [ 19 ]. We believe that our decision did not introduce a high risk of biased selection [ 20 ] because it is difficult to set reliable criteria for establishing the exact date of diagnosis and because there is a lot of variables in these cases in the official databases. Patients diagnosed with infectious and parasitic diseases are known to be at high risk of further infections due to their compromised immune systems and frequent contact with healthcare workers [ 1 , 18 ]. In our series, the 1,668 subjects of the infectious and parasitic diseases category (ICD9 CM 001-139) (5.79% of the total) were those at greatest risk of HAI. The results showed that cases within this diagnostic category had the highest risk (approximately 4 times higher) in the multivariable analysis (Table 2 , Fig. 2 ). In this category, the most common diagnoses were Escherichia coli sepsis (21%), Staphylococcus aureus sepsis (10%), SARS-CoV-2 (8%), Gram-negative bacterial sepsis (8%) and infectious colitis and gastroenteritis (6%) [ 1 ][ 18 ]. By quantifying the burden of comorbidity, CCI is a validated tool for stratifying patient risk and predicting long-term outcomes in hospitalised patients, including those with BS infections admitted to the ED [ 21 ]. CCI was recorded as severe (≥ 4) in 45.8% of cases, a prevalence (58.7%) significantly higher in the HAI group compared to the non-HAI group. It should be noted that a CCI of more than 4 (severe) was the second most important variable (OR 2.13, 95% CI 1.75–2.58) in predicting HAI. The inclusion of the moderate CCI category (CCI 3–4) in the logistic model as an independent variable strengthens the importance of the patient's risk profile in predicting HAIs. Most represented comorbidities were in order of frequency DM, ST, D, COPD, CAD, CKF, all of which were more represented in the HAI group than in the non-HAI group. Diabetes, in particular, a known risk factor for any infection, was found to increase the risk in cases of poor glycemic control and in the categories of older adults [ 9 ]. Subjects with genitourinary system diseases are most at risk of nosocomial infections [ 22 ]. In the latest ECDC report, the percentage of these cases ranged from 8.5% in Latvia to 30.0% in Iceland [ 1 ], while in our dataset they account for 7.7%, with twice the risk compared to all other hospitalised subjects. In light of reports of an increase in nosocomial infections during the COVID pandemic [ 23 ], we also considered the pandemic period as a variable potentially associated with an increased risk of nosocomial infection. The results confirmed this suspicion, indicating that the risk increased by an average of 37% (range 19% − 57%) (Table 2 ). The priority level assigned at triage was also associated to an increased risk of HAI. While overcrowding and long waiting times may mean more exposure to infection, our data do not confirm this. We can therefore conclude that it is the severity of cases and invasive emergency procedures and interventions that increase the risk of HAI, rather than overcrowding and prolonged EDLoS [ 7 ]. Advanced age [ 24 ] has been indicated as an important risk factor for nosocomial infections, e.g. urinary tract (UT) infections and gastrointestinal (GI) infections, while male gender [ 25 ] as a risk factor for bloodstream infections (BS). In our series, neither advanced age nor male gender were included in the model, despite the fact that over 40% of cases were over 80 years of age. This could be explained by the stepwise procedure of the logistic model favoring comorbidities (45.8% of cases had a CCI score of > 4) over advanced age. Some of these comorbidities, e.g., diabetes, cancer, memory loss and long-term lung problems [ 26 ], might be associated with a high likelihood of multiple invasive procedures and consequently an elevated infection risk. Limitations First: the data were collected from a single-centre, first-level ED. Results might not be replicable due to the facility's characteristics. Second, we used ICD-9-CM diagnosis codes to identify HAIs. Early studies showed these coded diagnoses had high specificity (≥ 93%), particularly for SS, Clostridium and BS infections. The percentage of HAI cases in our study (8.9%) is in line with the official ECDC registers. Case assessment bias may have inflated the estimate of the risk of HAIs, primarily because we created a posteriori estimate of cases [ 22 ], without having objective diagnostic tests in most cases. However, multivariate regression approaches that adjust the results for confounding variables should have contained any initial error [ 27 ]. The percentage of HAI cases in our study (8.9%) is in line with the official ECDC registers (7% -10% for the Italian registry). Case assessment bias may have inflated estimate of the risk of HAIs, primarily because we created a posteriori estimate of cases, without having objective diagnostic tests in most cases [ 22 ]. It should be noted, however, that the use of multivariate regression approaches that adjust the results for confounding variables should have contained any initial error. Third: The accuracy of our model is 0.697 [SE 0.011], with an optimal cutoff point of 0.517, a level of discriminatory power considered acceptable for clinical purposes [ 22 ]. Unfortunately, it cannot be expected that the group of variables available during the ED visit will have such a high impact on accurately predicting the patient's medium- and long-term prognosis [ 28 ]. In order to improve the statistical validity of the model, it is essential to minimise the risk of bias. This is achieved through careful selection of the variables to be tested, good outcome measures, careful analysis of the results, and elimination of the “self-fulfilling prophecy” effect [ 29 ]. Additional clinical events, such as complications and severity scores, with appropriate use of statistical models (e.g., survival analysis with inclusion of time to event or Bayesian models), could improve the predictive performance [ 22 ]. Conclusions A group of clinical characteristics in subjects visited in the ED predicts nosocomial infections. Those with bloodstream infections or pneumonia have a worse prognosis. Early identification of these patients could prevent complications and improve prognosis. Prospective multicenter studies could improve these findings. Abbreviations BS Blood Stream CAD Coronary Artery Disease CCI Charlson Comorbidity Index CHF Congestive Heart Failure CKD Hemiplegia, Chronic Kidney Disease COPD Chronic Obstructive Pulmonary Disease CTD Connective Tissue Disease CVA Acute Cerebrovascular Accidents or Transient Ischemic Attacks D Dementia DM Diabetes Mellitus ED Emergency Department GI Gastro-Intestinal CTD Connective Tissue Diseases, HAI Healthcare-associated Infection L Leukemia LD Liver Disease LY Lymphoma MI History of Myocardial Infarction HM History of hemiplegia NEWS National Early Warning Score PN Pneumonia PUD Peptic Ulcer Disease PVD Peripheral Vascular Disease ROC Receiver Operating Characteristic SS Surgical Site SST Skin & Soft Tissue ST Solid Tumors UT Urinary Tract Declarations Ethics approval and consent to participate The study was performed in accordance with the Declaration of Helsinki and the study protocol was approved by the Institutional Review Board (Comitato Etico della Romagna, Italy: Ref #9437/2019/I.5/268, 5 November 2021). The Institutional Review Board concluded that consent to participate was not required for this study protocol on the basis of Italian legislation (Ref. D.lgs. 30 June 2003, No. 196). Competing interests Andrea Fabbri, as the corresponding author for this article and also head of the emergency department of Forlì, the centre where the research was conducted: this could be considered as a competing interest. Barbara Benazzi, Roberto Martello as co-authors for this article and researchers of the center where the research was conducted could also be considered as a competing interest. Ayca Begum Tascioglu has no competing interests, Flavio Bertini has no competing interests, Danilo Montesi has no competing interests. Funding The study was supported by Regione Emilia Romagna, FIN-RER project 2020: Author Contribution A.F. had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the results. All authors were involved in the study concept and design and statistical analyses. A.B.T, F.B. and R.M. were responsible for the collection, management, analysis and interpretation of the data. A.F., A.B.T., F.B., B.B., R.M. and D.M. conducted the statistical analyses and drafted the manuscript. All authors contributed substantially to its revision and agree to be accountable for all aspects of the work. AF takes responsibility for the paper as a whole. Acknowledgement We are grateful to AUSL Romagna for helpful support Data Availability The data underlying this article may be shared on reasonable request to the corresponding author, within the limits of Italian legislation and if approved, by the General Direction of the Local Health Agency of Romagna, Italy. 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Additional Declarations No competing interests reported. Supplementary Files FigureA1.tiff TableA1.docx TableA2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7536446","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530562093,"identity":"009cd80f-61c3-43c8-8952-77e5f8f8b29a","order_by":0,"name":"Andrea 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16:52:11","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123132,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7536446/v1/342a3cbd6c27c771bca3a4df.html"},{"id":93882050,"identity":"c7424d13-38c3-43cf-872d-a7a0db2691ce","added_by":"auto","created_at":"2025-10-19 16:52:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62240,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of study patients during the study period (2017-2022).\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7536446/v1/cb923348ea0752dbaa0b93d4.png"},{"id":93883279,"identity":"8f6e9156-8a92-4e44-a1e0-c6f43e79459c","added_by":"auto","created_at":"2025-10-19 17:00:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41118,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the selected variables entered into the logistic model in the prediction of hospital-acquired infections (HAIs). Not selected features: neoplasms, priority level 3-4-5, disease of circulatory system, CCI\u0026lt;4, complications of pregnancy, childbirth, and the puerperium, congenital malformations, deformations, and chromosomal abnormalities, diseases of the blood and blood-forming organs, diseases of the digestive system, diseases of the musculoskeletal system and connective tissue, diseases of the nervous system and sense organs, diseases of the skin and subcutaneous tissue, external causes E and V, mental and behavioral disorders, symptoms, signs, and abnormal clinical and laboratory findings, ED length of stay, NEWS (for categories), age for decades, trauma related ED visit, gender: male\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7536446/v1/3ad8e741764b4ef5a1073a68.png"},{"id":93883522,"identity":"ee93ce59-5776-480b-bca6-7f0cb69d2734","added_by":"auto","created_at":"2025-10-19 17:08:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68852,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier survival function during hospital stay (up to 30 days) A: cumulative survival rate for HAI and non-HAI group; shaded areas cover 95% CI uncertainty. B: cumulative survival rate for the 6 different HAI categories. The log-rank test (Mantel–Cox test) was used to compare the survival distributions between different categories and the overall survival rate of the HAI group.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7536446/v1/71b76d65555577a2df2dcf6b.png"},{"id":104782662,"identity":"c58508f7-4ceb-4ba8-8d03-35423173e1eb","added_by":"auto","created_at":"2026-03-17 07:57:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1110825,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7536446/v1/11e012c5-ea79-44c3-8058-7a98dd6959a0.pdf"},{"id":93883280,"identity":"c8921c19-a78c-4a8c-b53d-64a80d3d2441","added_by":"auto","created_at":"2025-10-19 17:00:11","extension":"tiff","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":559718,"visible":true,"origin":"","legend":"","description":"","filename":"FigureA1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7536446/v1/e53ebed5815ddb821f818f63.tiff"},{"id":93883283,"identity":"122889e8-a76b-44a2-a9a9-49469e43a5e1","added_by":"auto","created_at":"2025-10-19 17:00:11","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":17330,"visible":true,"origin":"","legend":"","description":"","filename":"TableA1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7536446/v1/c1735382b3a483319a31ca4b.docx"},{"id":93883284,"identity":"5b32915e-4e5e-4728-aa6f-ff17e9beae04","added_by":"auto","created_at":"2025-10-19 17:00:11","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14973,"visible":true,"origin":"","legend":"","description":"","filename":"TableA2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7536446/v1/8db0aaf7463f130e0dd4df27.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eKey Predictive Features in the Emergency Department for Healthcare-Associated Infections\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eHealthcare-associated infections (HAIs), usually acquired in hospitals, are becoming a serious threat to healthcare systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Subjects with HAIs often have a longer hospital stay and experience adverse events during treatment, which can be caused by antibiotic resistance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This can lead to higher mortality rates and put a huge strain on healthcare systems [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe European Centre for Disease Prevention and Control (ECDC) annual surveillance reports [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] indicate that HAIs are most commonly pneumonia (PN), followed by urinary tract infections (UT), surgical site (SS) infections, bloodstream infections (BS), gastrointestinal infections (GI) and skin and soft tissue (SST) infections.\u003c/p\u003e\u003cp\u003eRisk factors include advanced age (\u0026gt;\u0026thinsp;50 years), invasive procedures, a high number of comorbidities, prolonged hospital stays, immunosuppression, non-appropriate use of antibiotics and multiple hospital transfers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Poor hand hygiene, inadequate aseptic techniques, and suboptimal environmental cleaning may further increase the risk [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA crowded environment, such as an ED, in which many patients with different complaints and undiagnosed diseases coexist for long periods in the same environment, can certainly increase the risk [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Identification of subjects at risk could allow for early diagnosis and treatment, thus reducing the impact of later complications and unfavorable outcome [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study aims to identify key clinical features in subjects visited in ED that predict the onset of nosocomial infections, based on the assumption that these subjects would have a worse outcome during their hospital stay.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\u003cp\u003eDuring the study period (1 January 2017\u0026ndash;31 December 2022), 283,385 electronic medical records from the ED of Morgagni-Pierantoni Hospital (a 3rd-level hospital 460 beds and serving a population of 200,000) in Forl\u0026igrave;, Italy, were evaluated. The analysis was performed on all hospitalised subjects after arrival in the ED, using a link between ED and inpatient records. 232,194 were excluded (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): 4,115 discharged homes directly from the ED; 4,064 died in the ED; 3,646\u0026thinsp;\u0026lt;\u0026thinsp;18 years; 2,103 insufficient data. A further 7,366 were excluded due to a hospital stay\u0026thinsp;\u0026lt;\u0026thinsp;3 days; 1,094 due to missing follow-up data. The final analysis was performed on a sample of 28,803 for whom all information from the initial ED visit to complete hospital records were available.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eMost baseline patient characteristics, including age (for decades) and sex, were collected at the time of ED registration. Vital signs at arrival in ED, specifically systolic blood pressure (SBP), heart rate (HR), respiratory rate (RR), and temperature, were recorded upon arrival to calculate the National Early Warning Score (NEWS) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. NEWS was computed and considered as a categorical variable (0\u0026ndash;4 low risk, 5\u0026ndash;6 medium risk, \u0026gt;\u0026thinsp;6 high risk).\u003c/p\u003e\u003cp\u003eThe Charlson Comorbidity Index (CCI) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] was calculated using free-text patient reports extracted during the ED visit. The CCI was categorised as follows (mild, 1\u0026ndash;2; moderate, 3\u0026ndash;4; severe, \u0026gt;\u0026thinsp;4), using selection criteria and disease categories recently validated [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, age adjustments were applied, with each decade over 40 years adding 1 point (e.g., 50\u0026ndash;59 years, +\u0026thinsp;1; 60\u0026ndash;69 years, +\u0026thinsp;2; 70\u0026ndash;79 years, +\u0026thinsp;3, etc.), with these \"age points\" added to the total CCI score [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eOutcome Measures\u003c/h3\u003e\n\u003cp\u003eHAIs during hospital stay were considered as main outcome measure, according to the ECDC disease categories: pneumonia (PN), bloodstream (BS) infections, urinary tract (UT) infections, gastrointestinal (GI) infections, surgical site (SS) infections and skin and soft tissue (SST) infections Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To reduce false positives, excluded were patients staying less than 3 days [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As HAI diagnoses were not associated with a definite date of identification, we estimated them by information derived from clinical, laboratory, microbiological and radiological reports in the electronic medical record. If a patient had more than one HAI, it was counted only once.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHealthcare associated infections (HAI) by category in subjects visited in ED. Data are reported in order of frequency as number of cases and percent (%). Patients with multiple HAI categories are counted only once in the analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHAI categories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eNo. = 2,256\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Stream (BS)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e995.91, 995.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSepsis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e26.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e038, 038.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSepticemia (including specific organisms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e22.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e038.8, 038.9, 790.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSepticemia unspecified, bacteriemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e038.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSepticemia due to Streptococcus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e038.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSepticemia due to Staphylococcus Aureus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e038.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSepticemia due to Gram neg. bacteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1,350\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e59.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUrinary tract (UT)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e599.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUrinary infection (unspecified site)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e41.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e590.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eAcute pyelonephritis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e996.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eInfection due to urinary catheter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e979\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e43.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePneumonia (PN)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBronchopneumonia, organism unspecified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e16.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e460\u0026ndash;466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eAcute \u0026amp; lower respiratory tract infections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePneumonia, organism unspecified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eViral pneumonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e482, 483, 484.7, 484.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003ePneumonia due to other organism, diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePneumococcal pneumonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e848\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e37.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGastrointestinal (GI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e008.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eIntestinal infection (Clostridium difficile)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e009.0, 009.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eInfectious enteritis, unspecified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e008.5x, 009.2, 099.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eOther intestinal infections due to bacteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e130\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSurgical Site (SS)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e996.6x\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eInfection due to internal prosthetic device\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e998.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eOther post-operative infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e682.xx\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eCellulitis and abscess (local SSI spread)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e998.50-4, 998.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePost operative wound infection,\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e94\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSkin \u0026amp; Soft Tissue (SST)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e680\u0026ndash;686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eOther cellulitis and abscess\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eData analysis was conducted on a cohort of 28,803 adult patients (\u0026gt;\u0026thinsp;18 years). Data were summarised as counts and percentages. Continuous variables were reported as either the mean (standard deviation, SD) or median [interquartile range, IQR]. Differences in patient characteristics, along with the corresponding 95% confidence intervals (CIs), were calculated using the Agresti-Caffo method [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For all statistical analyses, a p-value of less than 0.001 was considered significant.\u003c/p\u003e\u003cp\u003eThe primary outcome measure was any HAI. Demographic characteristics considered were: age, sex and comorbidities. Comorbidities were considered both as individual variables and as a categorised value of CCI\u0026thinsp;\u0026gt;\u0026thinsp;4. Additional variables tested were NEWS, ICD9-CM diagnosis codes, trauma and non-trauma related visits, ED length of stay (EDLoS) calculated as the difference between entry and exit times, as categorical values (\u0026lt;\u0026thinsp;6, 7\u0026ndash;12, 13\u0026ndash;24, \u0026gt;\u0026thinsp;24 hours), and different periods, pre- (2017\u0026ndash;2019) and COVID restriction (2020\u0026ndash;2022) periods.\u003c/p\u003e\u003cp\u003eA generalised linear mixed regression model was developed, adjusting for main characteristics such as age, sex, high CCI\u0026thinsp;\u0026gt;\u0026thinsp;4, NEWS\u0026thinsp;\u0026gt;\u0026thinsp;6 and trauma-related diseases. In a second step, the model was expanded to include the EDLoS, COVID period and main ICD9-CM diagnosis codes.\u003c/p\u003e\u003cp\u003eThe odds ratio (OR) and 95% confidence intervals (95% CI) were calculated. A score for mortality risk was calculated for each patient based on the coefficients computed by the logistic regression derived from variables entering the stepwise procedure. The accuracy of such a risk score was then evaluated by the area under the receiver operating characteristic (ROC) curves. The optimal cutoff point, i.e., the value that simultaneously maximises the sensitivity and the specificity, was calculated by the Youden index [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The Kaplan-Meier curve was reported to estimate the probability of survival at 30 days. The curve is plotted with time on the x-axis and the estimated probability of survival on the y-axis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The log-rank test (Mantel\u0026ndash;Cox test) was used to compare the survival distributions between different categories and the overall survival rate of the HAI group. To avoid ambiguity, no synthetic data were generated to address missing data; hence, the analysis utilised only complete cases.\u003c/p\u003e\u003cp\u003eThe statistical model was ultimately validated using event-number balancing techniques [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For feature selection, stepwise feature selection is used.\u003c/p\u003e\u003cp\u003eUsing a two-tailed binomial test, we estimated that, for a study with adequate statistical power and an acceptable margin of error of \u0026plusmn;\u0026thinsp;1%, assuming a significance level (α) of 5%, a power of 80%, and an expected prevalence of HAI cases of 7.7%, as reported in the ECDC last publication [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], the required initial sample size would be at least 2,731 patients, a lower number of cases than what was considered.\u003c/p\u003e\u003cp\u003eThe logistic model used a rule of thumb of at least 10 events per variable. A model with 10 to 20 predictors would require at least 200 cases of HAI. Assuming an incidence rate of 7.7%, this would be about 2,600 patients, in line with the previous estimate.\u003c/p\u003e\u003cp\u003eStatistical analyses were conducted in Python 3.10.12 using the following libraries: pandas (2.2.3), numpy (1.24.1), scipy (1.10.0), scikit-learn (1.6.0), seaborn (0.12.2), matplotlib (3.6.3), statsmodels (0.13.5), and lifelines (0.30.0).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStudy Subjects\u003c/h2\u003e\u003cp\u003eThe analysis was conducted on 28,803 patients (mean age, 73 years; SD, 17). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows patients\u0026rsquo; characteristics comparing HAI vs. non-HAI groups. A total of 2,556 cases (8.9%) belonged to the HAI group. There were no significant differences between men and women or between age groups, except that subjects over 80 were more represented in the HAI group. Patients in the HAI group were characterized by a higher NEWS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e), as well as a higher comorbidity index (CCI), particularly when the CCI was greater than 4 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The list of comorbidities is shown in \u003cb\u003eTable A1.\u003c/b\u003e The most common comorbidities were diabetes mellitus (DM), heart failure (HF), dementia (D), chronic obstructive pulmonary disease (COPD), coronary artery disease (CAD) and chronic kidney failure (CKD); all of them were more prevalent in the HAI group (\u003cb\u003eTable A1\u003c/b\u003e). Length of stay in ED was less than 6 hours in most cases (99.5%), with no difference between groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics at entry in ED in relation to the clinical profile of subjects with/without hospital acquired infections (HAIs).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal, No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHAI group, No. (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-HAI group, No. (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatients\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28,803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,556 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26,247 (91.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e (males)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14,365 (49.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,281 (50.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13,084 (49.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.01 (0.93\u0026ndash;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e853 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e828 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.30 (0.20\u0026ndash;0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e31\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e896 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e868 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32 (0.22\u0026ndash;0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e41\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,659 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,577 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.52 (0.41\u0026ndash;0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,558 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e160 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,398 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66 (0.56\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e61\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,940 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e300 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,640 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83 (0.73\u0026ndash;0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e71\u0026ndash;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,878 (23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e624 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6,254 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03 (0.94\u0026ndash;1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12,019 (41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,337 (52.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10,682 (40.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.60 (1.47\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNEWS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18,374 (63.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,586 (62.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16,788 (64.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.92 (0.85\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,432 (25.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e676 (26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6,756 (25.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.04 (0.95\u0026ndash;1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,997 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e294 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,703 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.13 (1.00\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCCI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,563 (22.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e311 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6,252 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.44 (0.39\u0026ndash;0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,051 (31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e744 (29.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8,307 (31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89 (0.81\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13,189 (45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,501 (58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11,688 (44.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.77 (1.63\u0026ndash;1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are reported as N. of cases (%), difference as Odds Ratio (OR) with 95% Confidence Intervals (95% CI). NEWS: New Early Warning Score, CCI: Charlson Comorbidity Index. Percent of subjects referred to the total number of cases. P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for significance.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMain diagnoses in ED, divided by ICD-9 CM category, are reported in \u003cb\u003eTable A2\u003c/b\u003e. They were in order of frequency, circulatory system diseases (22.1%), respiratory system diseases (19.2%), digestive system diseases (14.5%), and injuries and poisonings (12.5%). The prevalence of infectious and parasitic diseases and genitourinary diseases was higher in the HAI group, whereas respiratory diseases, injuries, poisonings and digestive diseases were more prevalent in the non-HAI group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of selected variables entered the logistic model for hospital acquired infections (HAI) reported in order of relevance. Charlson Comorbidity Index (CCI). Variables were considered as categorical and data are reported as Odds Ratio (OR) with 95% Confidence Intervals (95%CI); P value for significance\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndependent variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfectious \u0026amp; Parasitic Diseases (001-139)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.94 (3.09 to 5.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCI\u0026thinsp;\u0026gt;\u0026thinsp;4 (severe)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.13 (1.75 to 2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiseases of the Genitourinary System (580\u0026ndash;629)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.98 (1.55 to 2.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCI 3\u0026ndash;4 (moderate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.66 (1.35 to 2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOVID-19 Period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.37 (1.19 to 1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePriority Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.27 (1.10 to 1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNot selected variables: length of stay in ED, NEWS, trauma, ICD9-CM diagnosis codes of neoplasm (140\u0026ndash;239), endocrine, nutritional, and metabolic diseases (240\u0026ndash;279), diseases of the blood (280\u0026ndash;289), mental disorders (290\u0026ndash;319), diseases of the nervous system (320\u0026ndash;389), diseases of the circulatory system (390\u0026ndash;459), diseases of respiratory system (460\u0026ndash;519), diagnosis codes of digestive system (520\u0026ndash;579), complications of pregnancy (630\u0026ndash;679), diseases of the skin (680\u0026ndash;709), diseases of the musculoskeletal system (710\u0026ndash;739), congenital malformations (740\u0026ndash;759), symptoms, signs, and laboratory findings (780\u0026ndash;799), injury, poisoning (800\u0026ndash;999), external causes (E, V code).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMain Results\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e represent the list of key predictive features selected by the stepwise procedure of the logistic model to predict nosocomial infections. In order of importance, they were: CCI score\u0026thinsp;\u0026gt;\u0026thinsp;4, infectious and parasitic diseases, the CCI score 2\u0026ndash;4, diseases of the genito-urinary system, the period of COVID pandemic, and priority level at entry.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003eA1\u003c/span\u003e shows the ROC curve for the risk score of HAI occurrence. The accuracy of predicting HAI was 0.697\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011, with a sensitivity of 0.63 and a specificity of 0.67 at an optimal cut-off point (the best sensitivity at the best specificity) of 0.517.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the estimated 30-day Kaplan\u0026ndash;Meier cumulative survival rate. The cumulative survival rate was lower in the HAI group than in the non-HAI group (log-rank test P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cb\u003eA area\u003c/b\u003e), and cases in the bloodstream infection and pneumonia categories had a significantly lower survival rate compared to the survival of the overall HAI group (\u003cb\u003eB area\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis is the largest study to date on predicting HAI in ED patients. The risk is associated with specific conditions, e.g., infectious/parasitic diseases, high comorbidity index, genito-urinary system diseases, the pandemic period, and high priority at presentation to the ED. Survival rates for those with HAI, particularly bloodstream and pneumonia, were lower. These findings emphasise the importance of identifying risk factors for nosocomial infections to improve patient outcomes.\u003c/p\u003e\u003cp\u003eOlder age, higher comorbidity burden and higher illness severity were associated with diagnosis of HAI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].The results of this study demonstrate that this pattern of key features is valid for predicting the risk of HAI, independently of the reason(s) for attending ED for different complaints.\u003c/p\u003e\u003cp\u003eThe study protocol excluded 7,885 cases (16.7%) from the analysis cases as the hospital stay was \u0026lt;\u0026thinsp;3 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This decision was made both to reduce the confounding effect of cases with infection already present at the time of admission [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] The percentage of cases with a diagnosis of HAI at admission is probably not negligible: the ECDC in the last report estimates this percentage ranges from 15.6% (Czech Republic) to 41.2% (Sweden) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We believe that our decision did not introduce a high risk of biased selection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] because it is difficult to set reliable criteria for establishing the exact date of diagnosis and because there is a lot of variables in these cases in the official databases.\u003c/p\u003e\u003cp\u003ePatients diagnosed with infectious and parasitic diseases are known to be at high risk of further infections due to their compromised immune systems and frequent contact with healthcare workers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In our series, the 1,668 subjects of the infectious and parasitic diseases category (ICD9 CM 001-139) (5.79% of the total) were those at greatest risk of HAI. The results showed that cases within this diagnostic category had the highest risk (approximately 4 times higher) in the multivariable analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In this category, the most common diagnoses were Escherichia coli sepsis (21%), Staphylococcus aureus sepsis (10%), SARS-CoV-2 (8%), Gram-negative bacterial sepsis (8%) and infectious colitis and gastroenteritis (6%) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBy quantifying the burden of comorbidity, CCI is a validated tool for stratifying patient risk and predicting long-term outcomes in hospitalised patients, including those with BS infections admitted to the ED [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. CCI was recorded as severe (\u0026ge;\u0026thinsp;4) in 45.8% of cases, a prevalence (58.7%) significantly higher in the HAI group compared to the non-HAI group. It should be noted that a CCI of more than 4 (severe) was the second most important variable (OR 2.13, 95% CI 1.75\u0026ndash;2.58) in predicting HAI. The inclusion of the moderate CCI category (CCI 3\u0026ndash;4) in the logistic model as an independent variable strengthens the importance of the patient's risk profile in predicting HAIs.\u003c/p\u003e\u003cp\u003eMost represented comorbidities were in order of frequency DM, ST, D, COPD, CAD, CKF, all of which were more represented in the HAI group than in the non-HAI group. Diabetes, in particular, a known risk factor for any infection, was found to increase the risk in cases of poor glycemic control and in the categories of older adults [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSubjects with genitourinary system diseases are most at risk of nosocomial infections [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In the latest ECDC report, the percentage of these cases ranged from 8.5% in Latvia to 30.0% in Iceland [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], while in our dataset they account for 7.7%, with twice the risk compared to all other hospitalised subjects.\u003c/p\u003e\u003cp\u003eIn light of reports of an increase in nosocomial infections during the COVID pandemic [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], we also considered the pandemic period as a variable potentially associated with an increased risk of nosocomial infection. The results confirmed this suspicion, indicating that the risk increased by an average of 37% (range 19% \u0026minus;\u0026thinsp;57%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe priority level assigned at triage was also associated to an increased risk of HAI. While overcrowding and long waiting times may mean more exposure to infection, our data do not confirm this. We can therefore conclude that it is the severity of cases and invasive emergency procedures and interventions that increase the risk of HAI, rather than overcrowding and prolonged EDLoS [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdvanced age [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] has been indicated as an important risk factor for nosocomial infections, e.g. urinary tract (UT) infections and gastrointestinal (GI) infections, while male gender [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] as a risk factor for bloodstream infections (BS). In our series, neither advanced age nor male gender were included in the model, despite the fact that over 40% of cases were over 80 years of age. This could be explained by the stepwise procedure of the logistic model favoring comorbidities (45.8% of cases had a CCI score of \u0026gt;\u0026thinsp;4) over advanced age. Some of these comorbidities, e.g., diabetes, cancer, memory loss and long-term lung problems [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], might be associated with a high likelihood of multiple invasive procedures and consequently an elevated infection risk.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eFirst: the data were collected from a single-centre, first-level ED. Results might not be replicable due to the facility's characteristics. Second, we used ICD-9-CM diagnosis codes to identify HAIs. Early studies showed these coded diagnoses had high specificity (\u0026ge;\u0026thinsp;93%), particularly for SS, Clostridium and BS infections.\u003c/p\u003e\u003cp\u003eThe percentage of HAI cases in our study (8.9%) is in line with the official ECDC registers. Case assessment bias may have inflated the estimate of the risk of HAIs, primarily because we created a posteriori estimate of cases [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], without having objective diagnostic tests in most cases. However, multivariate regression approaches that adjust the results for confounding variables should have contained any initial error [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The percentage of HAI cases in our study (8.9%) is in line with the official ECDC registers (7% -10% for the Italian registry). Case assessment bias may have inflated estimate of the risk of HAIs, primarily because we created a posteriori estimate of cases, without having objective diagnostic tests in most cases [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. It should be noted, however, that the use of multivariate regression approaches that adjust the results for confounding variables should have contained any initial error.\u003c/p\u003e\u003cp\u003eThird: The accuracy of our model is 0.697 [SE 0.011], with an optimal cutoff point of 0.517, a level of discriminatory power considered acceptable for clinical purposes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Unfortunately, it cannot be expected that the group of variables available during the ED visit will have such a high impact on accurately predicting the patient's medium- and long-term prognosis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In order to improve the statistical validity of the model, it is essential to minimise the risk of bias. This is achieved through careful selection of the variables to be tested, good outcome measures, careful analysis of the results, and elimination of the \u0026ldquo;self-fulfilling prophecy\u0026rdquo; effect [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additional clinical events, such as complications and severity scores, with appropriate use of statistical models (e.g., survival analysis with inclusion of time to event or Bayesian models), could improve the predictive performance [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eA group of clinical characteristics in subjects visited in the ED predicts nosocomial infections. Those with bloodstream infections or pneumonia have a worse prognosis. Early identification of these patients could prevent complications and improve prognosis. Prospective multicenter studies could improve these findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBS Blood Stream\u003c/p\u003e\u003cp\u003eCAD Coronary Artery Disease\u003c/p\u003e\u003cp\u003eCCI Charlson Comorbidity Index\u003c/p\u003e\u003cp\u003eCHF Congestive Heart Failure\u003c/p\u003e\u003cp\u003eCKD Hemiplegia, Chronic Kidney Disease\u003c/p\u003e\u003cp\u003eCOPD Chronic Obstructive Pulmonary Disease\u003c/p\u003e\u003cp\u003eCTD Connective Tissue Disease\u003c/p\u003e\u003cp\u003eCVA Acute Cerebrovascular Accidents or Transient Ischemic Attacks\u003c/p\u003e\u003cp\u003eD Dementia\u003c/p\u003e\u003cp\u003eDM Diabetes Mellitus\u003c/p\u003e\u003cp\u003eED Emergency Department\u003c/p\u003e\u003cp\u003eGI Gastro-Intestinal\u003c/p\u003e\u003cp\u003eCTD Connective Tissue Diseases,\u003c/p\u003e\u003cp\u003eHAI Healthcare-associated Infection\u003c/p\u003e\u003cp\u003eL Leukemia\u003c/p\u003e\u003cp\u003eLD Liver Disease\u003c/p\u003e\u003cp\u003eLY Lymphoma\u003c/p\u003e\u003cp\u003eMI History of Myocardial Infarction\u003c/p\u003e\u003cp\u003eHM History of hemiplegia\u003c/p\u003e\u003cp\u003eNEWS National Early Warning Score\u003c/p\u003e\u003cp\u003ePN Pneumonia\u003c/p\u003e\u003cp\u003ePUD Peptic Ulcer Disease\u003c/p\u003e\u003cp\u003ePVD Peripheral Vascular Disease\u003c/p\u003e\u003cp\u003eROC Receiver Operating Characteristic\u003c/p\u003e\u003cp\u003eSS Surgical Site\u003c/p\u003e\u003cp\u003eSST Skin \u0026amp; Soft Tissue\u003c/p\u003e\u003cp\u003eST Solid Tumors\u003c/p\u003e\u003cp\u003eUT Urinary Tract\u003c/p\u003e"},{"header":"Declarations","content":"\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e The study was performed in accordance with the Declaration of Helsinki and the study protocol was approved by the Institutional Review Board (Comitato Etico della Romagna, Italy: Ref #9437/2019/I.5/268, 5 November 2021). The Institutional Review Board concluded that consent to participate was not required for this study protocol on the basis of Italian legislation (Ref. D.lgs. 30 June 2003, No. 196).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eAndrea Fabbri, as the corresponding author for this article and also head of the emergency department of Forl\u0026igrave;, the centre where the research was conducted: this could be considered as a competing interest. Barbara Benazzi, Roberto Martello as co-authors for this article and researchers of the center where the research was conducted could also be considered as a competing interest. Ayca Begum Tascioglu has no competing interests, Flavio Bertini has no competing interests, Danilo Montesi has no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe study was supported by Regione Emilia Romagna, FIN-RER project 2020:\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.F. had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the results. All authors were involved in the study concept and design and statistical analyses. A.B.T, F.B. and R.M. were responsible for the collection, management, analysis and interpretation of the data. A.F., A.B.T., F.B., B.B., R.M. and D.M. conducted the statistical analyses and drafted the manuscript. All authors contributed substantially to its revision and agree to be accountable for all aspects of the work. AF takes responsibility for the paper as a whole.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to AUSL Romagna for helpful support\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data underlying this article may be shared on reasonable request to the corresponding author, within the limits of Italian legislation and if approved, by the General Direction of the Local Health Agency of Romagna, Italy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePoint prevalence survey of healthcare-associated. infections and antimicrobial use in European acute care hospitals \u0026ndash; 2022\u0026ndash;2023 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ecdc.europa.eu/en/publications-data/PPS-HAI-AMR-acute-care-europe-2022-2023\u003c/span\u003e\u003cspan address=\"https://www.ecdc.europa.eu/en/publications-data/PPS-HAI-AMR-acute-care-europe-2022-2023\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed March 13, 2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarrasa-Villar JI, Aibar-Rem\u0026oacute;n C, Prieto-Andr\u0026eacute;s P, Mareca-Do\u0026ntilde;ate R, Moliner-Lahoz J. 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Infect Control Hosp Epidemiol. 2015;36:1089\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/ice.2015.129\u003c/span\u003e\u003cspan address=\"10.1017/ice.2015.129\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Older Age, Predictors, Comorbidity, Survival Rate, Nosocomial Infections","lastPublishedDoi":"10.21203/rs.3.rs-7536446/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7536446/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eOvercrowding, prolonged stays and invasive interventions could increase the risk of healthcare-associated infections (HAIs) in Emergency Departments (ED). Aim of study was to investigate whether the risk of developing a HAI can be estimated in patients at entry on the basis of ED visit data, and whether they are associated with poorer outcome.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective single centre study included subjects who required urgent hospitalisation following ED visit between 2017 and 2022. Main outcome measures considered were the occurrence of late HAIs and the cumulative survival rate at 30 days. The key predictive features tested in a logistic model were age, sex, vital parameters as measured by the National Early Warning Score (NEWS), priority levels upon entry, comorbidities by the Charlson Comorbidity Index (CCI), trauma related diseases, main diagnosis and ED length of stay.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn 2,556 (8,9%) out of 28,803 hospitalised patients aged 73 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] years (mean [SD]) a diagnosis of HAI was recorded during hospital stays. In order of frequency, HAIs occurred in bloodstream (4.7%), in urinary (3.4%), respiratory (2.9%), gastrointestinal (0.4%) tract, or in surgical (0.3%) and skin and soft tissue (0.05%) sites. Main features selected by the logistic model in the prediction of HAI were infectious and parasitic diseases, CCI\u0026thinsp;\u0026gt;\u0026thinsp;4, genitourinary system diseases, CCI 3 to 4, COVID period, priority level at arrival in ED. In-hospital cumulative survival rate in HAI group was reduced, namely for subjects with pneumonia and bloodstream infections.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eA group of key characteristics in subjects visiting the ED can predict the onset of nosocomial infections that negatively affect survival, particularly for patients who develop pneumonia or bloodstream infections.\u003c/p\u003e","manuscriptTitle":"Key Predictive Features in the Emergency Department for Healthcare-Associated Infections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-19 16:52:06","doi":"10.21203/rs.3.rs-7536446/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70119c8d-185e-4605-b182-48f4cd9c8bf3","owner":[],"postedDate":"October 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T05:55:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-19 16:52:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7536446","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7536446","identity":"rs-7536446","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}